A sensor network, such as a wireless sensor network, typically includes spatially distributed autonomous sensor nodes to monitor physical or environmental conditions, such as temperature, humidity, pressure, sound, vibration, motion or pollutants. Applications range from habitat monitoring and traffic control to surveillance. Each sensor node includes one or more sensors, and is typically equipped with a radio transceiver or other wireless communication device as well as a power source such as a battery. Sensor readings are transmitted by the sensor nodes and received by a data sink, or data collection device, either directly or via one or more other sensor nodes. The received sensor readings are processed by the data sink or forwarded on by the data sink to a network, computing device, or communication device.
In general, large scale sensor data gathering is accomplished through multi-hop routing from individual sensor nodes to the data sink. Successful deployment of such large scale sensor networks typically faces two major challenges, namely: reduction of global communication cost and energy consumption load balancing.
The need for global communication cost reduction arises from the fact that such sensor networks typically include hundreds to thousands of sensors, generating tremendous amount of sensor data to be delivered to the data sink. It is very much desired to take full advantage of the correlations among the sensor data to reduce the cost of communication. Existing approaches adopt in-network data compressions, such as entropy coding or transform coding, to reduce global traffic. However, these approaches tend to introduce significant computation and control overheads that often are not suitable for sensor network applications.
The need for energy consumption load balancing arises from the fact that large-scale sensor networks typically require multi-hop data transmission. FIG. 1 illustrates a large-scale wireless sensor network 100 where sensors are densely deployed in the region of interest to monitor the environment on a regular basis. Suppose N sensors, denoted as s1, s2, s3 . . . sN form a multi-hop route to the data sink 102, with dj denoting the readings obtained by the sensor in node sj. A typical way of transmitting dj, j=1, 2, 3 . . . N to the data sink 102 is through multi-hop relay as depicted in FIG. 1. In particular, node s1 transmits sensor reading d1 to s2, node s2 transmits sensor readings d2 from its own sensor as well as the relayed reading d1 to node s3, and so on. At the end of the route, node sN transmits all N readings to the data sink 102. It can be observed that the closer a sensor node is to the data sink, the more energy is consumed. This is because not only the node transmits the readings of its own sensor but also all the relayed sensor readings. As a result, the sensor nodes closer to the data sink 102 tend to run out of energy, and the lifetime of the sensor network will be significantly shortened.